The present invention relates generally to image identification systems. More specifically, the present invention relates to methods and procedures for improving the performance and reliability of image identification systems.
Image identification systems have been used in the past, one application being biometric image identification systems. One type of biometric image identification system is a fingerprint identification system. In a fingerprint identification system, a user places the tip of a finger on a scanning surface of a fingerprint image reader device. Each ridge of the epidermis (outer skin) is dotted with sweat glands that produce moisture that, in combination with oily secretions and other substances naturally present on the tip of a finger, enable an image of a fingerprint to be scanned. (The present invention can also be successfully applied to images generated from readers that do not rely on the moisture content of the skin to capture an image.). The fingerprint image reader device creates an image scan by capturing a picture of fingerprint ridge characteristics present on the tip of a finger. In many systems, the image is then compared to a database of other stored fingerprint images or fingerprint image models for verification, authentication, or some other form of analysis.
Security systems that implement fingerprint identification technology have the potential of being reliable and easy to use. These benefits arise from the fact that the technology does not require a system user to retain any piece of knowledge, such as a password, personal identification number, combination or any other code. Neither must a user possess a card, key or any other physical device to gain access to a secured environment. A fingerprint security authentication key, as opposed to a knowledge or possession based security authentication key is nearly impossible to lose, steal, or be forgotten.
Development of practical security system applications that incorporate fingerprint image identification technology has been hindered by a general non-repeatability of data from one image scan to another. In particular, physical variations present in the environment of a fingerprint reader device can cause substantial incongruities from one image scan of a fingerprint as compared to a subsequently taken image scan of the same fingerprint. Differences in the temperature, amount of pressure applied to the scanning surface, moisture content of the finger, as well as the effects of medications and differences in blood pressure can all contribute to substantial incongruities from one image scan to another. These incongruous results hinder the development of most fingerprint identification technology applications because inconsistent data leads to an unacceptably high number of false acceptances (multiple identifications, which include matching to wrong people) and false rejections (not recognizing an enrolled user) for applications that might require instantaneous and unsupervised comparisons to be made between a scanned fingerprint image and a database of fingerprint images or fingerprint models. Another problem associated with many image identification systems is the small amount of data gleaned by the typical system from each image. For instance, most fingerprint identification systems are minutiae-based, typically meaning that only rods, islands, and bifurcations are cataloged and made available for analysis. An ideal image scan performed by a minutiae-based system will typically glean a maximum of approximately 50 useful data points, and this count may be further compromised by data points that might not appear in the scanned image due to previously discussed interference in the image reader environment. The discrimination capability of the typical minutiae-based identification system is not adequate for applications that require instantaneous and accurate comparisons to be made between a real-time scanned image and a database of potential matching images or models. In addition, systems that glean only a small number of useful data points are susceptible to fraudulently produced fingerprint forgeries.
Yet another problem associated with the average image identification system is that they prove to be an inefficient model for making comparisons between a real-time scanned image and a database of potential matching images or models. Most systems compare the real-time scanned image or model derived from that scan with each of the images or models contained within a database of images or models on a one-to-one basis until a matching pair is located. Depending on the size of the database, the time required to locate a matching pair can be substantial.
Due to these classical limitations on image identification technology, image identification applications have typically been limited to use in low security and/or supervised environments within which quick processing is not a priority. For instance, many law enforcement agencies that currently utilize fingerprint identification systems operate within the confines of minutiae-based matching. A minutiae-based system may be adequate in such an environment where a fingerprint expert may be available to take the time necessary to supervise the system and act as the arbiter in cases of multiple matches to an online database.
Minutiae-based systems, and other traditional fingerprint identification systems, are not adequate for unsupervised mass market applications, such as an automatic teller machine (ATM) that incorporates a fingerprint identification system and requires the user to submit a valid fingerprint scan when using an ATM card to make a money transaction. Neither are traditional systems appropriate for authentication systems designed to selectively and instantaneously provide access to places and devices such as computers, computer networks, facilities, automobiles and appliances based on the receipt of an authorized image. Efficient and effective functionality of these types of applications depend on a level of rapid and accurate analysis that cannot be consistently achieved by the traditional fingerprint image identification system.
Another benefit associated with an authentication system that incorporates image identification is that such a system is tunable, meaning the discrimination level or the match requirements during image comparison can be adjusted based on the nature of the environment to be secured and the desired level of security associated therewith. Due to burdens of non-repeatability of data, false match acceptances, and false match rejections, the range and number of levels within which a traditional image identification system can be tuned is narrowly limited. Such a system may not be tunable at all. Even the highest level of discrimination in a traditional system provides a substantially limited amount of discrimination.